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Soft actuators have demonstrated potential in a range of applications, including soft robotics, artificial muscles, and biomimetic devices. However, the majority of current soft actuators suffer from the lack of real‐time sensory feedback, prohibiting their effective sensing and multitask function. Here, a promising strategy is reported to design bilayer electrothermal actuators capable of simultaneous actuation and sensation (i.e., self‐sensing actuators), merely through two input electric terminals. Decoupled electrothermal stimulation and strain sensation is achieved by the optimal combination of graphite microparticles and carbon nanotubes (CNTs) in the form of hybrid films. By finely tuning the charge transport properties of hybrid films, the signal‐to‐noise ratio (SNR) of self‐sensing actuators is remarkably enhanced to over 66. As a result, self‐sensing actuators can actively track their displacement and distinguish the touch of soft and hard objects. Proposed self‐sensing actuators offer simultaneous actuation and sensation through two input electric terminals. Decoupled electrothermal stimulation and strain sensation are achieved by the hybridization of graphite microparticles and carbon nanotubes. The nearly zero thermal coefficient of resistance and high piezoresistive response of hybrid films significantly enhance the signal‐to‐noise ratio of the proposed self‐sensing actuators.

Designing advanced soft robots with soft sensing capabilities for real‐world applications remains challenging due to the intricate integration of actuation and sensor capabilities, which require diverse materials and complex procedures. This paper introduces a fabric‐based robotic technology featuring an “all textile‐based self‐sensing pneumatic actuator” and a low‐cost resistive strain sensor created through simple sewing techniques. The novel approach eliminates the need for additional strain‐limiting woven fabric, simplifying the manufacturing process. It also enables the development of bioinspired motions such as bending, twisting, and snake‐like movements. The electromechanical behaviors of the sensor and bending actuator are tested for their performance under positive air pressure. Through mathematical modeling, the actuator's sensing capacity is estimated accurately, providing precise feedback for pressure and position control. Different closed‐loop controller types, including On–Off and Proportional Integral Derivative (PID) control, are evaluated for their effectiveness. Furthermore, the practical application of the sensing actuator is demonstrated by integrating it into a wearable glove, showcasing its enhanced sensing capabilities for finger‐like soft wearable robotic applications. This research tackles the challenges associated with designing advanced soft robots with integrated sensing capabilities, offering a promising fabric‐based solution that can drive significant advancements in real‐world applications. This study focuses on the design, fabrication, characterization, modeling, and control of a textile‐based resistive sensing robotic actuator. A novel approach is introduced to create mechanical anisotropy, eliminating the limitations posed by strain‐limiting fabric. The integrated sensor provides accurate strain measurements for closed‐loop control in soft robotics. The research findings contribute to flexible and adaptable robotic systems with broad applications.

Designing soft robots that are able to perceive unstructured, dynamic environments and their deformations has been a long-term goal. Previously reported self-sensing soft actuators were mostly constructed via integrating separate actuators and sensors. The actuation performances and the sensing reliability are affected owing to the unmatched materials and weak connections. Realizing a seamless integration of soft actuators and sensors remains a grand challenge. Here, we report a fabrication strategy to endow soft actuators with sensing capability and programmable actuation performances. The foam inside the actuator functions as actuator and sensor simultaneously, effectively addressing the conformability and connection reliability issues that existed in current self-sensing actuators. The actuators are lightweight (a decrease of 58% in weight), powerful (lifting a load of 433 times of its own weight), and versatile (coupling twisting and contraction motions). Furthermore, the actuators are able to detect multiple physical stimuli with high reliability, demonstrating their exteroception and proprioception capability. Two self-sensing soft robotic prototypes, including a bionic bicep and a bionic neck, are constructed to illustrate their multifunctionality. Our study opens up new possibilities for the design of soft actuators and has promising potential in a variety of applications, ranging from human-robot interaction, soft orthotics, to wearable robotics.

Soft artificial muscles are increasingly important for applications in haptics, endoscopic robotics, and humanoid systems. Unlike rigid actuators, which follow deterministic motion paths, soft actuators adaptively deform under external forces, posing significant challenges for accurate control. Conventional approaches rely on external sensors to improve performance, but these add weight, cost, and integration complexity. This article introduces the self‐sensing soft hydraulic muscle (SSHM), a novel actuator that intrinsically provides real‐time feedback of both length and force without the need for external sensors. A robust theoretical model is developed to characterize SSHM behavior and accurately estimate actuation parameters. Experimental validation shows high precision, with root mean square errors of 0.3 N for force and 0.69 mm for length. The study further examines the influence of working fluids on SSHM performance. Among the tested liquids, propylene glycol is identified as optimal, doubling durability compared to water while minimizing hysteresis (0.86%). To demonstrate practical utility, SSHM is integrated into a 3D‐printed humanoid elbow joint, achieving an RMSE of 3.19° for joint angle estimation and 0.45 N for external force sensing. These results highlight SSHM as a compact, adaptive, and scalable platform, advancing soft robotics by unifying actuation and sensing in a single structure.

The vibration isolation system is now indispensable to high-precision instruments and equipment, which can provide a low vibration environment to ensure performance. However, the disturbance with variable frequency poses a challenge to the vibration isolation system, resulting in precision reduction of dynamic modeling. This paper presents a velocity self-sensing method and experimental verification of a vibration isolation system. A self-sensing actuator is designed to isolate the vibration with varying frequencies according to the dynamic vibration absorber structure. The mechanical structure of the actuator is illustrated, and the dynamic model is derived. Then a self-sensing method is proposed to adjust the anti-resonance frequency of the system without velocity sensors, which can also reduce the complexity of the system and prevent the disturbance transmitting along the cables. The self-sensing controller is constructed to track the variable frequency of the disturbance. A prototype of the isolation system equipped with velocity sensors is developed for the experiment. The experiment results show that the closed-loop transmissibility is less than −5 dB in the whole frequency rand and is less than −40 dB around, adding anti-resonance frequency which can be adjusted from 0 Hz to initial anti-resonance frequency. The disturbance amplitude of the payload can be suppressed to 10%.

Smart tools are limited by actuation–sensing integration and structural redundancy, making it difficult to achieve compactness, ultra-precision feed, and immediate feedback. This paper proposes a self-sensing giant magnetostrictive actuator-based turning tool (SSGMT), which enables simultaneous actuation and output sensing without external sensors. A multi-objective optimization model is first established to determine the key design parameters of the SSGMT to improve magnetic transfer efficiency, system compactness, and sensing signal quality. Then, a dynamic hysteresis model with a Hammerstein structure is developed to capture its nonlinear characteristics. To ensure accurate positioning and a robust response, a hybrid control strategy combining feedforward compensation and adaptive feedback is implemented. The SSGMT is experimentally validated through a series of tests including self-sensing displacement accuracy and trajectory tracking under various frequencies and temperatures. The prototype achieves nanometer-level resolution, stable output, and precise tracking across different operating conditions. These results confirm the feasibility and effectiveness of integrating actuation and sensing in one structure, providing a promising solution for the application of smart turning tools.

Soft robots and devices exploit deformable materials that are capable of changes in shape to allow conformable physical contact for controlled manipulation. While the use of embedded sensors in soft actuation systems is gaining increasing interest, there are limited examples where the body of the actuator or robot is able to act as the sensing element. In addition, the conventional feedforward control method is widely used for the design of a controller, resulting in imprecise position control from a sensory input. In this work, we fabricate a soft self-sensing finger actuator using flexible carbon fibre-based piezoresistive composites to achieve an inherent sensing functionality and design a dual-closed-loop control system for precise actuator position control. The resistance change of the actuator body was used to monitor deformation and fed back to the motion controller. The experimental and simulated results demonstrated the effectiveness, robustness and good controllability of the soft finger actuator. Our work explores the emerging influence of inherently piezoresistive soft actuators to address the challenges of self-sensing, actuation and control, which can benefit the design of next-generation soft robots.

Multi‐stimuli‐responsive/‐functional polymeric materials can respond to numerous stimuli and execute multiple tasks, overcoming barriers faced by single‐stimuli materials. Herein, the development of hybrid piezoelectric–magnetic self‐sensing actuator (HPMSA) that can both sense and actuate is proposed. This iron oxide/functionalized carbon nanotube/polyvinylidene fluoride film optimizes both piezoelectric and magnetic properties through dual‐alignment fabrication, utilizing strong element bonds for simultaneous alignment. Magnetic nanoparticles are advantageous over nanorods due to latter's randomized shape anisotropy decreasing magnetization. The dual magnetic and mechanical processing increases polar β ‐crystal content to 88%, where magnetic alignment alone increases degree of crystallinity to 66%. As a vibration damper, HPMSA operates within 40–600 Hz frequency, with a sensing sensitivity of 2.5 mV g −1 and 0.72 m s −2 weighted acceleration damping, lowering passenger health risks. Piezoelectric and magnetic relationship shows 0.19 V increase with 125 mT applied. The flexible HPMSA can integrate onto a curved surface and sense/dampen vibrations of an air motor, propeller drone, and simulated tremors. The HPMSA provides tremendous potential and understanding into multi‐stimuli‐responsive/functional materials, simultaneous alignment, and vibration control in the next generation of transportation vehicles for human safety.

The present work aims to design a new polarized piezoelectric sensor actuator. This sensor actuator not only can achieve sensing and execution at the same time, but also has the advantages of small size and high integration. A mathematical model of stack displacement with input voltage and number of layers was established. The relationship between the charge generated by quartz crystal and external force was numerically analysed. The static and modal analysis of the structure were carried out to obtain the measured force range and the maximum working frequency. The transient simulation is used to verify the follow-up law of the actuator to the alternating signal. The Electric field interference analysis of the piezoelectric sensor actuator was performed to eliminate the interference from the electric field generated by the actuator. The results show that the measured force range of the actuator is up to 1 kN, with the maximum working frequency is 1000 Hz, and the actuator can follow the drive voltage signal well, with an almost negligible electric field interference in the sensor part. This paper provides a new method and theory for the study of self-sensing actuators.

Signal processing using orthogonal cutting force components for tool condition monitoring has established itself in literature. In the application of single axis strain sensors however a linear combination of cutting force components has to be processed in order to monitor tool wear. This situation may arise when a single axis piezoelectric actuator is simultaneously used as an actuator and a sensor, e.g. its vibration control feedback signal exploited for monitoring purposes. The current paper therefore compares processing of a linear combination of cutting force components to the reference case of processing orthogonal components. Reconstruction of the dynamic force acting at the tool tip from signals obtained during measurements using a strain gauge instrumented tool holder in a turning process is described. An application of this dynamic force signal was simulated on a filter-model of that tool holder that would carry a self-sensing actuator. For comparison of the orthogonal and unidirectional force component tool wear monitoring strategies the same time-delay neural network structure has been applied. Wear-sensitive features are determined by wavelet packet analysis to provide information for tool wear estimation. The probability of a difference less than 5 percentage points between the flank wear estimation errors of above mentioned two processing strategies is at least 95 %. This suggests the viability of simultaneous monitoring and control by using a self-sensing actuator.